| Information through a Spiking Neuron (1996) | |||||||||||||||||
Abstract | |||||||||||||||||
| While it is generally agreed that neurons transmit information about their synaptic inputs through spike trains, the code by which this information is transmitted is not well understood. An upper bound on the information encoded is obtained by hypothesizing that the precise timing of each spike conveys information. Here we develop a general approach to quantifying the information carried by spike trains under this hypothesis, and apply it to the leaky integrate-and-fire (IF) model of neuronal dynamics. We formulate the problem in terms of the probability distribution p(T ) of interspike intervals (ISIs), assuming that spikes are detected with arbitrary but finite temporal resolution. In the absence of added noise, all the variability in the ISIs could encode information, and the information rate is simply the entropy of the ISI distribution, H(T ) = h\Gammap(T ) log 2 p(T )i, times the spike rate. H(T ) thus provides an exact expression for the information rate. The methods developed ... | |||||||||||||||||
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